41.SVC_Poly.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2ItrIBZcDNV"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "442def04-422d-451a-87c1-3ef72dfcde68"
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      "source": [
        "df = pd.read_csv(\"logisticRegressionSubmit.csv\")\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
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              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>Fail</td>\n",
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              "      <th>1</th>\n",
              "      <td>36</td>\n",
              "      <td>Pass</td>\n",
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              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>Pass</td>\n",
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              "      <td>Pass</td>\n",
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              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "      <td>Fail</td>\n",
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              "    X     Y\n",
              "0  12  Fail\n",
              "1  36  Pass\n",
              "2  37  Pass\n",
              "3  47  Pass\n",
              "4  10  Fail"
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      "source": [
        "df"
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      "execution_count": 3,
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              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
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              "  </thead>\n",
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              "      <td>Fail</td>\n",
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              "      <td>36</td>\n",
              "      <td>Pass</td>\n",
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              "      <td>Pass</td>\n",
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              "      <td>10</td>\n",
              "      <td>Fail</td>\n",
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              "      <td>10</td>\n",
              "      <td>Fail</td>\n",
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              "      <th>6</th>\n",
              "      <td>96</td>\n",
              "      <td>Pass</td>\n",
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              "      <th>7</th>\n",
              "      <td>23</td>\n",
              "      <td>Fail</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>51</td>\n",
              "      <td>Pass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>72</td>\n",
              "      <td>Pass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>65</td>\n",
              "      <td>Pass</td>\n",
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              "      <td>15</td>\n",
              "      <td>Fail</td>\n",
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              "      <th>12</th>\n",
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              "      <td>Fail</td>\n",
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              "      <th>13</th>\n",
              "      <td>45</td>\n",
              "      <td>Pass</td>\n",
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              "      <th>14</th>\n",
              "      <td>50</td>\n",
              "      <td>Pass</td>\n",
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            "text/plain": [
              "     X     Y\n",
              "0   12  Fail\n",
              "1   36  Pass\n",
              "2   37  Pass\n",
              "3   47  Pass\n",
              "4   10  Fail\n",
              "5   10  Fail\n",
              "6   96  Pass\n",
              "7   23  Fail\n",
              "8   51  Pass\n",
              "9   72  Pass\n",
              "10  65  Pass\n",
              "11  15  Fail\n",
              "12  29  Fail\n",
              "13  45  Pass\n",
              "14  50  Pass"
            ]
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      },
      "source": [
        "X=df.iloc[:,:-1].values\n",
        "X"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12],\n",
              "       [36],\n",
              "       [37],\n",
              "       [47],\n",
              "       [10],\n",
              "       [10],\n",
              "       [96],\n",
              "       [23],\n",
              "       [51],\n",
              "       [72],\n",
              "       [65],\n",
              "       [15],\n",
              "       [29],\n",
              "       [45],\n",
              "       [50]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vqbyYApc0Gj",
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        },
        "outputId": "7a3ca3ea-cf30-4521-e94c-a93716bee919"
      },
      "source": [
        "Y=df.iloc[:,-1].values\n",
        "Y"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass', 'Fail',\n",
              "       'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass', 'Pass'],\n",
              "      dtype=object)"
            ]
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          "execution_count": 5
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    {
      "cell_type": "code",
      "metadata": {
        "id": "7Sb3qexhc4xx"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=4,test_size = 0.25)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qZv12gTc94_"
      },
      "source": [
        "clf =  SVC(kernel = 'poly', random_state = 0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ua7yPRwdFEZ",
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        },
        "outputId": "f3cb3e8d-5525-4c62-a566-155bdec19b10"
      },
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='poly',\n",
              "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9AzFi3EdPDu"
      },
      "source": [
        "pred=clf.predict(x_test)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "931pxNlVgB5-",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3feeb460-629d-4bdc-b811-d6b3495f96e1"
      },
      "source": [
        "y_test, pred"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['Fail', 'Fail', 'Pass', 'Pass'], dtype=object),\n",
              " array(['Fail', 'Fail', 'Pass', 'Pass'], dtype=object))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbRWxE3qhi_P"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    }
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}